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The Beginner’s Guide to Intelligent Document Processing (IDP)

By 2025, the world’s data repository will reach 175 zettabytes. The bulk will be unstructured like images, emails, and PDFs, requiring intelligent software to organize the data into actionable insights. Fortunately, Intelligent Document Processing (IDP) technology already exists to meet this incredible need. IDP offers a variety of public and private sector uses, and organizations are starting to take notice.

Here we’ll review Intelligent Document Processing basics, how the technology works, and the real-world applications it offers for businesses today.

What is Intelligent Document Processing?

Intelligent document processing (IDP) uses artificial intelligence (AI) and machine learning algorithms to read, understand, and extract data from documents. The software then organizes and validates the data to reveal actional insights.

IDP applications are critical in paper-reliant industries, such as insurance, healthcare, banking, finance, and government. With incredible efficiency and accuracy, thetechnology easilyprocesses unstructured data, such as invoices, sales orders, and customer correspondence. As a result, businesses find they can do more with fewer resources or devote current resources to higher-level tasks.

Companies can train IDP systems to read various documents, such as receipts, tax forms, ID cards, contracts, invoices, etc. Different industries can also customize the systems to extract specific data typesbased on what they need, such as names, addresses, dates, and prices.

How Intelligent Document Processing Works for Businesses:

Data collection:
The first step in intelligent document processing is to ingest data from a variety of sources, both digital and paper-based. Fortunately, most IDP solutions seamlessly unify with businesses’ existing programs. So, for instance, organizations can quickly integrate IDP software with their scanner system to automatically transform hard copies into digital format.

Pre-processing:
After IDP software collects the document details, it moves onto the pre-processing stage,where it analyses and cleans up the data, making it ready for further processing. Pre-processing involves several tasks such as Optical Character Recognition (OCR) for extracting text from images, de-skewing pages, image enhancement, and more.

Classification:
Next, IDP software identifies the document type, such as an invoice vs.a tax return, so thatit can apply rule-based tasks. So after identifying a loan application, for example, the software automatically identifies personal information such as names, addresses, dates of birth, bank statements, tax returns, pay stubs, and credit history. Natural language processing algorithms help the software analyze these elements and ensure it only captures relevant data.

Extraction:
After classification, IDP systems start extracting information using various methods like template-based extraction, rules-based extraction, and machine learning. For example, machine learning models use pre-processed and classified documentation to gather detailed information, such as dates, names, or figures. The aim is to automatically pull out all the critical data points from the document so they can be stored for further analysis.

Post-processing and validation:
After the extract, machine learning IDP models clean up the data. For example, the software can correct common misspellings or adjust words to standard formatting. Ultimately the software conducts several manuals and automated validation checks to verify the accuracy.

Integration:
Traditional document capture systems extract data and drop the information into content management systems with no ability to deliver insights. Modern IDP software, however, integrates with a variety of downstream applications (like ERPs) and workflows (like loans management) where it can help businesses make informed decisions. For example, using embedded AI tools, IDP software automatically identifies related data within enterprise content management systems, like ERPs, or even predicts whether the loan will be approved.

The Technologies behind Intelligent Document Processing

The technology pillars powering IDP include artificial intelligence and optical character recognition (OCR), which contains several subdisciplines of AI, such as computer vision (CV) and natural language processing (NLP). In addition, intelligent document processing solutions work well with robotic process automation(RPA). Let’s look at each technology in further depth.

  • Optical character recognition: Optical character recognition (OCR) converts typed, printed, and handwritten text into a computer-readable form. Although OCR solutions have some intelligence, they merely interpret what they “see” and thus only decipher the documents’ meaning. As a result, OCR relies on AI to derive insights from the data.
  • Computer vision: Computer vision (CV) is a subset of AI that deals with understanding and extracting meaning from digital images. Unlike OCR, which focuses on text recognition, CV can analyze the layout of a document to identify and extract data from non-textual elements like tables or graphs.
  • Natural language processing: Natural language processing (NLP) is a subset of AI that extract meaning from unstructured data. So instead of staff spending hours sifting through support tickets to find common challenges, NLP allows businesses to see the data in seconds. Companies often combine NLP with OCR to improve accuracy
  • Artificial intelligence: Artificial intelligence is the science of building, training, and deploying software models that mimic human intellect. AI models can make their own judgments and predictions once trained on vast historical data. As a result, the models learn to “understand” imaging information and investigate the significance of textual information like people do.
  • Robotic process automation: Robotic process automation complements Intelligent Document Processing (IDP) since it is not a part of the technology stack. RPA bots can execute activities such as transaction processing, data manipulation, triggered responses, and interaction with other business IT systems.

Intelligent Document Processing Applications Across Industries

Intelligent document processing (IDP) solutions are designed to meet the specific needs of individual businesses and industries.

Accounting
Accounting is a field that generates significant paperwork and documents. With IDP technology, organizations can rapidly extract data and use it to make the best decisions.

For instance, businesses can teach IDP software to identify aspects of invoices and automatically extract key details like date, price, and vendor name. With that, teams can drastically improve efficiency, and businesses can save significant costs.

We saw this first hand after implementing DoqumentAI in a global organization’s accounts payable department. By cutting manual processing requirements, we helped the organization gain 95% time savings and significant cost savings.

Medical Records:
Practitioners struggle to organize vast amounts of patient information while simultaneously running busy practices. Performing administrative tasks, managing medical records, and ensuring that document processing is HIPAA-compliant while seeing patients is daunting. That’s why typical physicians spend more than two-thirds of their time doing paperwork! Fortunately, Healthcare institutions using IDP solutions may seamlessly organize records and diagnostics, access the information, when needed, and harness AI to improve patient treatments and outcomes.

Transportation & Supply Chain:
Supply chain companies can use Intelligent document processing (IDP) to streamline many operations. For example, IDP can automatically extract data from shipping documents, which can then be useful to track shipments and inventory levels. IDP can also generate bills of lading and process customs declarations, so goods travel faster across borders. And those examples just scratch the surface of IDP supply chain capabilities.

qBotica’s IDP solution, DoqumentAI,helped a Supply Chain Software company process 500+ documentsdailyso it could vastly improve its customer quoting process. The machine learning-powered software rapidly read through customer emails, interpreted the requests, extracted the valuable content, and instantly prepared quotes with the help of automated robots.

Getting Started with IDP

Getting the correct data to the right people at the right time is critical for success, whether through analytics or document archiving. Fortunately, IDP solutions help organizations automate and streamline variousworkflows, saving significant time and money. Additionally, by harnessing AI, IDP helps businesses make smarter decisions, opening new profit opportunities.

Schedule a demo with our team today to see how IDP can benefit your unique business needs.

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